Upon completing this Course successfully, participants will be able to:

• Describe the key economic, psychological and motivational concepts that influence reward
• Discuss the role of reward strategies and policies in an organization
• Contribute to the administration of employee reward policies and processes
• Differentiate between the factors that influence employee satisfaction with the reward system
  Defend the case for non financial rewards in attracting, retaining and motivating people
• Design a strategic reward plan

A variety of teaching methodologies will be employed These methodologies are designed to engage participants, facilitate active learning, and enable the practical application of concepts
-Mini lectures
-Case Study Analysis
-Group Discussions
-Feedback and Reflection Sessions

DAY 1

BASICS OF INSIGHT GENERATION DIFFERENT DATA SCIENCE FIELDS
Morning Session : Basics of Insight Generation
Data analytics is the new sliced bread
Introduction to data analytics
The importance and impact of data analytics in modern business
Case Study Successful implementation of data analytics in a retail
company
Data value chain
Understanding the data lifecycle collection, processing, analysis, and
presentation
Case Study Data value chain in a logistics company
Tools for generating insights
Overview of tools Excel, SQL, Python, R, Tableau
Hands on Activity Using Excel and SQL for basic data analysis
Business intelligence and data mining
Differences and intersections between BI and data mining
Case Study Data mining techniques in fraud detection

Afternoon Session The Different Data Science Fields
Analysis vs analytics
Defining analysis and analytics, their scope, and applications
Why are there so many data science catchwords?
Understanding the jargon analytics, data science, BI, ML, AI
Introduction to Business Analytics
Fundamentals and applications in decision making
Introduction to Data Analytics
Techniques and tools used in data analytics
Introduction to Data Science
Key concepts, methodologies, and tools
Introduction to Business Intelligence (Machine Learning, Artificial Intelligence)
Overview of BI, ML, and AI
Hands on Activity Simple data analysis with BI tools
BI vs ML vs AI key characteristics and differences
Comparative analysis of BI, ML, and AI
Case Study Application of BI, ML, and AI in healthcare

DAY 2

INTRODUCTION TO DATA AND DATA SCIENCE COMMON
DATA SCIENCE TECHNIQUES
Morning Session : Introduction to Data and Data Science
What is data science?
Definition, scope, and significance
History of data vs information vs data science
Evolution from data collection to data science
What is the purpose of data science fields?
Objectives and outcomes of data science
Why do we need data science disciplines?
The importance of data science in modern business
Case Study Data science in predictive maintenance

Afternoon Session : Common Data Science Techniques
Traditional Data Techniques Real life Examples
Descriptive statistics, regression analysis
Case Study Traditional data analysis in market research
Big Data Techniques Real life Examples
Hadoop, Spark, NoSQL databases
Case Study Big data techniques in social media analysis
Business Intelligence ( Techniques Real life Examples
Dashboards, reporting, data warehousing
Case Study BI in financial reporting
Traditional Methods Techniques Real life Examples
Time series analysis, hypothesis testing
Case Study Traditional methods in quality control
Machine Learning ( Techniques, Types Real life Examples
Supervised, unsupervised, and reinforcement learning
Case Study Machine learning in customer segmentation

DAY 3

COMMON DATA SCIENCE TOOLS BASIC STATISTICS
Morning Session : Common Data Science Tools
All the tools needed in business intelligence analytics and data science
Overview of essential tools Python, R, SQL, Tableau, Power BI
Hands on Activity Introduction to Python and R for data analysis
Data Science Jobs What do they involve and what to look out for?
Roles and responsibilities in data science careers
Interactive Discussion Career paths and job market trends
Dispelling common misconceptions
Addressing myths and misunderstandings about data science
Population vs sample
Understanding the difference and its importance in analysis
Case Study Sampling techniques in survey research

Afternoon Session : Basic Statistics Foundations of Quantitative Insights
Basic statistics
Mean, median, mode, standard deviation, variance
Types of variables
Nominal, ordinal, interval, and ratio scales
Measures of central tendency
Calculation and interpretation
Measures of dispersion
Range, interquartile range, variance, standard deviation
Hands on Activity Statistical analysis using Excel

DAY 4

THE NORMAL DISTRIBUTION AND HISTOGRAMS DATA VISUALIZATION
Morning Session : The Normal Distribution and Histograms
Normal distribution and histograms
Characteristics of normal distribution, creating histograms
The empirical rule
Applying the 68 95 99 7 rule
Covariance and correlation
Understanding and calculating covariance and correlation
Case Study Correlation analysis in investment portfolios

Afternoon Session : Data Visualization
Data visualization and Anscombe s Quartet
Importance of visualization, lesson from Anscombe s Quartet
Data Cleaning using Tableau
Techniques for cleaning and preparing data in Tableau
Bar chart and heat maps
Creating and interpreting bar charts and heat maps
Hands on Activity Creating visualizations in Tableau

DAY 5

ADVANCED CHARTS AND DASHBOARDS DEMAND FORECASTING
Morning Session : Advanced Charts and Dashboards
Bar in bar graph and bullet graph visualization
Advanced visualization techniques
Hands on Activity Creating bar in bar and bullet graphs
Generating insights from social media data
Techniques and tools for social media analytics
Case Study Social media analytics in brand management
Dashboards
Designing and implementing interactive dashboards
Hands on Activity Creating dashboards in Power BI

Afternoon Session : Demand Forecasting
Regression analysis
Techniques and applications of regression analysis
Demand forecasting
Methods and tools for accurate demand forecasting
Demand forecasting smoothing methods
Techniques for smoothing and improving forecast accuracy
Case Study Demand forecasting in supply chain management